Autonomous computations that rely on automated reaction network elucidation algorithms
may pave the way to make computational catalysis on a par with experimental research in …

Automating molecular design using deep reinforcement learning (RL) has the potential to
greatly accelerate the search for novel materials. Despite recent progress on leveraging …

Due to their lack of maturity, models for evaluating sensitivities from molecular structure have
so far been relatively little used for the design of new energetic materials. The selection of …

Haptic feedback has been considered to be a facilitator for learning in multiple ways. On the
one hand, the haptic feedback can connect multiple representations and scaffold their …

By applying machine learning to molecular design, researchers aim to tame vast molecular
search spaces and accelerate the discovery of useful structures. A notable new approach …